def hfr(station, var_list, full=False, plots=False, diagnostics=False):
"""
Run through the variables and pass to the High Flag Rate Check
:param Station station: Station Object for the station
:param list var_list: list of variables to test
:param bool full: run a full update (unused here)
:param book plots: turn on plots
:param bool diagnostics: turn on diagnostic output
:returns: int : number of variables on which these flags have been set
"""
vars_set = [] # Keep track of where these flags are set.
for var in var_list:
obs_var = getattr(station, var)
flags, any_set = high_flag_rate(obs_var,
plots=plots,
diagnostics=diagnostics)
obs_var.flags = utils.insert_flags(obs_var.flags, flags)
if any_set:
vars_set += [var]
# Now double check the list of variables where "H" flags have been set.
# If one of a synergistic pair is, then do the other (wind speed/direction,
# sea/station level pressure).
# Using exclusive or. This only passes if one is True and the other is False.
if ("sea_level_pressure" in vars_set) is not ("station_level_pressure"
in vars_set):
if "sea_level_pressure" in vars_set:
set_synergistic_flags(station, "station_level_pressure")
elif "station_level_pressure" in vars_set:
set_synergistic_flags(station, "sea_level_pressure")
if ("wind_speed" in vars_set) is not ("wind_direction" in vars_set):
if "wind_speed" in vars_set:
set_synergistic_flags(station, "wind_direction")
elif "wind_direction" in vars_set:
set_synergistic_flags(station, "wind_speed")
# For synergistically flagged, just count once, so this return is correct.
return len(vars_set) # hfr
def identify_multiple_values(obs_var,
times,
config_file,
plots=False,
diagnostics=False):
"""
Use config_file to read in critical values, and then assess to find
:param MetVar obs_var: meteorological variable object
:param array times: array of times (usually in minutes)
:param str config_file: configuration file to store critical values
:param bool plots: turn on plots
:param bool diagnostics: turn on diagnostic output
"""
# TODO check works with missing data (compressed?)
# TODO monthly?
flags = np.array(["" for i in range(obs_var.data.shape[0])])
time_diffs = np.ma.diff(times) / np.timedelta64(1,
"m") # presuming minutes
value_diffs = np.ma.diff(obs_var.data)
multiple_obs_at_time, = np.where(time_diffs == 0)
# if diagnostics:
# print("number of identical timestamps {}".format(multiple_obs_at_time.shape[0]))
suspect_locs, = np.ma.where(value_diffs[multiple_obs_at_time] != 0)
# set the first of the obs, then the second which make the diff
flags[multiple_obs_at_time[suspect_locs]] = "T"
flags[multiple_obs_at_time[suspect_locs] + 1] = "T"
obs_var.flags = utils.insert_flags(obs_var.flags, flags)
if diagnostics:
print("Timestamp {}".format(obs_var.name))
print(" Cumulative number of flags set: {}".format(
len(np.where(flags != "")[0])))
return # identify_multiple_values
def mcu(station, var_list, full=False, plots=False, diagnostics=False):
"""
Run through the variables and pass to monthly clean up
:param Station station: Station Object for the station
:param list var_list: list of variables to test
:param bool full: run a full update (unused here)
:param book plots: turn on plots
:param bool diagnostics: turn on diagnostic output
"""
for var in var_list:
obs_var = getattr(station, var)
flags = clean_up(obs_var, station, plots=plots, diagnostics=diagnostics)
obs_var.flags = utils.insert_flags(obs_var.flags, flags)
return # wrc
def set_synergistic_flags(station, var):
"""
Set the flags on a synergistic variable.
:param Station station: Station Object for the station
:param str var: name of variable
"""
obs_var = getattr(station, var)
new_flags = np.array(["" for i in range(obs_var.data.shape[0])])
old_flags = obs_var.flags
obs_locs, = np.where(obs_var.data.mask == False)
if obs_locs.shape[0] > 10 * utils.DATA_COUNT_THRESHOLD:
# require sufficient observations to make a flagged fraction useful.
# As synergistically flagged, add to all flags.
new_flags[obs_locs] = "H"
obs_var.flags = utils.insert_flags(obs_var.flags, new_flags)
return # set_synergistic_flags
def wrc(station, var_list, full=False, plots=False, diagnostics=False):
"""
Run through the variables and pass to the World Record Check.
:param Station station: Station Object for the station
:param list var_list: list of variables to test
:param bool full: run a full update (unused here)
:param book plots: turn on plots
:param bool diagnostics: turn on diagnostic output
"""
for var in var_list:
obs_var = getattr(station, var)
flags = record_check(obs_var,
station.continent,
plots=plots,
diagnostics=diagnostics)
obs_var.flags = utils.insert_flags(obs_var.flags, flags)
return # wrc
def lc(station, var_list, full=False, plots=False, diagnostics=False):
"""
Run through the variables and pass to the Logic Checks
:param Station station: Station Object for the station
:param list var_list: list of variables to test
:param bool full: run a full update (unused here)
:param book plots: turn on plots
:param bool diagnostics: turn on diagnostic output
"""
# https://github.com/glamod/glamod-dm/blob/master/glamod-parser/glamod/parser/filters/observations_table.py
# database parser has these, for future reference
# station level (from inventory listing, not for each timestamp)
return_code = 0
if station.lat < -90 or station.lat > 90:
write_logic_error(station,
"Bad latitude: {}".format(station.lat),
diagnostics=diagnostics)
if diagnostics:
print("Bad latitude: {}".format(station.lat))
return_code = -1
if station.lon < -180 or station.lon > 180:
write_logic_error(station,
"Bad longtitude: {}".format(station.lon),
diagnostics=diagnostics)
if diagnostics:
print("Bad longtitude: {}".format(station.lon))
return_code = -1
if station.lon == 0 and station.lat == 0:
write_logic_error(
station,
"Bad longtitude & latitude combination: lon={}, lat={}".format(
station.lon, station.lat),
diagnostics=diagnostics)
if diagnostics:
print("Bad longtitude/latitude: {} & {}".format(
station.lon, station.lat))
return_code = -1
# Missing elevation acceptable - removed this for the moment (7 November 2019, RJHD)
# missing could be -999, -999.9, -999.999 or even 9999.0 etc hence using string comparison
if (station.elev < -432.65 or station.elev > 8850.):
if str(station.elev)[:4] not in ["-999", "9999"]:
write_logic_error(station,
"Bad elevation: {}".format(station.elev),
diagnostics=diagnostics)
if diagnostics:
print("Bad elevation: {}".format(station.elev))
return_code = -1
else:
if diagnostics:
print("Missing elevation, but not flagged: {}".format(
station.elev))
if station.times.iloc[0] < dt.datetime(1650, 1, 1):
write_logic_error(station,
"Bad start time: {}".format(station.times[0]),
diagnostics=diagnostics)
if diagnostics:
print("Bad start time: {}".format(station.times[0]))
return_code = -1
elif station.times.iloc[-1] > dt.datetime.now():
write_logic_error(station,
"Bad end time: {}".format(station.times[-1]),
diagnostics=diagnostics)
if diagnostics:
print("Bad end time: {}".format(station.times[-1]))
return_code = -1
# observation level
for var in var_list:
obs_var = getattr(station, var)
flags = logic_check(obs_var, plots=plots, diagnostics=diagnostics)
obs_var.flags = utils.insert_flags(obs_var.flags, flags)
return return_code # lc
def identify_spikes(obs_var,
times,
config_file,
plots=False,
diagnostics=False):
"""
Use config_file to read in critical values, and then assess to find spikes
:param MetVar obs_var: meteorological variable object
:param array times: array of times (usually in minutes)
:param str config_file: configuration file to store critical values
:param bool plots: turn on plots
:param bool diagnostics: turn on diagnostic output
"""
# TODO check works with missing data (compressed?)
# TODO monthly?
masked_times = np.ma.masked_array(times, mask=obs_var.data.mask)
time_diffs = np.ma.diff(masked_times) / np.timedelta64(
1, "m") # presuming minutes
value_diffs = np.ma.diff(obs_var.data)
if len(value_diffs.mask.shape) == 0:
# single mask value, replace with array of True/False's
if value_diffs.mask:
value_diffs.mask = np.ones(value_diffs.shape)
else:
value_diffs.mask = np.zeros(value_diffs.shape)
# get thresholds for each unique time differences
unique_diffs = np.unique(time_diffs.compressed())
# retrieve the critical values
critical_values = {}
for t_diff in unique_diffs:
try:
c_value = utils.read_qc_config(config_file,
"SPIKE-{}".format(obs_var.name),
"{}".format(t_diff))
critical_values[t_diff] = float(c_value)
except KeyError:
# no critical value for this time difference
pass
# if none have been read, give an option to calculate in case that was the reason for none
if len(critical_values) == 0:
get_critical_values(obs_var,
times,
config_file,
plots=plots,
diagnostics=diagnostics)
# and try again
for t_diff in unique_diffs:
try:
c_value = utils.read_qc_config(config_file,
"SPIKE-{}".format(obs_var.name),
"{}".format(t_diff))
critical_values[t_diff] = float(c_value)
except KeyError:
# no critical value for this time difference
pass
# pre select for each time difference that can be tested
for t_diff in unique_diffs:
if t_diff == 0:
# not a spike or jump, but 2 values at the same time.
# should be zero value difference, so fitting histogram not going to work
# handled in separate test
continue
# new blank flag array
flags = np.array(["" for i in range(obs_var.data.shape[0])])
t_locs, = np.where(time_diffs == t_diff)
try:
c_locs, = np.where(
np.abs(value_diffs[t_locs]) > critical_values[t_diff])
except:
# no critical value for this time difference
continue # to next loop
# TODO - sort spikes at very beginning or very end of sequence,
# when don't have a departure from/return to a normal level
# potential spikes
for ps, possible_in_spike in enumerate(t_locs[c_locs]):
is_spike = False
spike_len = 1
while spike_len <= MAX_SPIKE_LENGTH:
# test for each possible length to see if identified
try:
out_spike_t_diff = time_diffs[possible_in_spike +
spike_len]
possible_out_spike = value_diffs[possible_in_spike +
spike_len]
except IndexError:
# got to end of data run, can't test final value at the moment
break
# need to test mask/unmasked using array rather than values extracted above
# as if values unmasked, then no mask attribute to test!
if time_diffs.mask[possible_in_spike + spike_len] == False and \
value_diffs.mask[possible_in_spike + spike_len] == False:
try:
# find critical value for time-difference of way out of spike
out_critical_value = critical_values[out_spike_t_diff]
except KeyError:
# don't have a value for this time difference, so use the maximum of all as a proxy
out_critical_value = max(critical_values.values())
else:
# time or value difference masked
out_critical_value = max(critical_values.values())
if np.abs(possible_out_spike) > out_critical_value:
# check that the signs are opposite
if np.sign(value_diffs[possible_in_spike]) != np.sign(
value_diffs[possible_in_spike + spike_len]):
is_spike = True
break
spike_len += 1
if is_spike and spike_len >= 1:
# test within spike differences (chosing correct time difference)
within = 1
while within < spike_len:
within_t_diff = time_diffs[possible_in_spike + within]
if time_diffs.mask[possible_in_spike + within] == False:
try:
within_critical_value = critical_values[
within_t_diff]
if value_diffs[
possible_in_spike +
within] > within_critical_value / 2.:
is_spike = False
except KeyError:
# don't have a value for this time difference, so use the maximum of all as a proxy
within_critical_value = max(
critical_values.values())
else:
# time difference masked
within_critical_value = max(critical_values.values())
if value_diffs.mask[possible_in_spike + within] == False:
if value_diffs[possible_in_spike +
within] > within_critical_value / 2.:
is_spike = False
else:
# if masked then no data, so can't say if it's not a spike
pass
within += 1
if is_spike:
# test either side (either before or after is too big)
try:
before_t_diff = time_diffs[possible_in_spike - 1]
if time_diffs.mask[possible_in_spike - 1] == False:
before_critical_value = critical_values[before_t_diff]
else:
# time difference masked
before_critical_value = max(critical_values.values())
except KeyError:
# don't have a value for this time difference, so use the maximum of all as a proxy
before_critical_value = max(critical_values.values())
except IndexError:
# off the front of the data array
before_critical_value = max(critical_values.values())
try:
after_t_diff = time_diffs[possible_in_spike + spike_len +
1]
if time_diffs.mask[possible_in_spike + spike_len +
1] == False:
after_critical_value = critical_values[after_t_diff]
else:
# time difference masked
after_critical_value = max(critical_values.values())
except KeyError:
# don't have a value for this time difference, so use the maximum of all as a proxy
after_critical_value = max(critical_values.values())
except IndexError:
# off the back of the data array
after_critical_value = max(critical_values.values())
try:
if value_diffs.mask[possible_in_spike - 1] == False:
if value_diffs[possible_in_spike -
1] > before_critical_value / 2.:
# before spike fails test
is_spike = False
except IndexError:
# off the front of the data array
pass
try:
if value_diffs.mask[possible_in_spike + spike_len +
1] == False:
if value_diffs[possible_in_spike + spike_len +
1] > after_critical_value / 2.:
# after spike fails test
is_spike = False
except IndexError:
# off the back of the data array
pass
# if the spike is still set, set the flags
if is_spike:
# "+1" because of difference arrays
flags[possible_in_spike + 1:possible_in_spike + 1 +
spike_len] = "S"
# diagnostic plots
if plots:
plot_spike(times, obs_var, possible_in_spike + 1,
spike_len)
obs_var.flags = utils.insert_flags(obs_var.flags, flags)
if diagnostics:
print("Spike {}".format(obs_var.name))
print(" Time Difference: {} minutes".format(t_diff))
print(" Cumulative number of flags set: {}".format(
len(np.where(flags != "")[0])))
return # identify_spikes
def neighbour_outlier(target_station,
initial_neighbours,
variable,
diagnostics=False,
plots=False,
full=False):
"""
Works on a single station and variable. Reads in neighbour's data, finds locations where sufficent are sufficiently different.
:param Station target_station: station to run on
:param array initial_neighbours: input neighbours (ID, distance) pairs
:param str variable: obs variable being run on
:param bool diagnostics: print extra material to screen
:param bool plots: create plots from each test
:param bool full: run full reprocessing rather than using stored values.
"""
station_list = utils.get_station_list()
# if sufficient
n_neighbours = len(np.where(initial_neighbours[:, 0] != "-")[0]) - 1
if n_neighbours < utils.MIN_NEIGHBOURS:
print("{} has insufficient neighbours ({}<{})".format(
target_station.id, n_neighbours, utils.MIN_NEIGHBOURS))
else:
#*************************
# extract target observations
obs_var = getattr(target_station, variable)
flags = np.array(["" for i in range(obs_var.data.shape[0])
]).astype("<U10")
#*************************
# read in in the neighbour (buddy) data
all_buddy_data = np.ma.zeros(
[len(initial_neighbours[:, 0]),
len(target_station.times)])
all_buddy_data.mask = np.ones(all_buddy_data.shape)
for bid, buddy_id in enumerate(initial_neighbours[:, 0]):
if buddy_id == target_station.id:
# first entry is self
continue
if buddy_id == "-":
# end of the list of buddies
break
if diagnostics:
print("{}/{} {}".format(bid, len(initial_neighbours[:, 0]),
buddy_id))
# set up station object to hold information
buddy_idx, = np.where(station_list.id == buddy_id)
buddy = utils.Station(buddy_id, station_list.iloc[buddy_idx].latitude.values[0], \
station_list.iloc[buddy_idx].longitude.values[0], station_list.iloc[buddy_idx].elevation.values[0])
try:
buddy, buddy_df = io.read_station(os.path.join(
setup.SUBDAILY_PROC_DIR, "{:11s}.qff".format(buddy_id)),
buddy,
read_flags=True)
buddy_var = getattr(buddy, variable)
# apply flags
flag_locs, = np.where(buddy_var.flags != "")
buddy_var.data.mask[flag_locs] = True
except OSError as e:
# file missing, move on to next in sequence
io.write_error(
target_station,
"File Missing (Buddy, {}) - {}".format(variable, buddy_id))
continue
except ValueError as e:
# some issue in the raw file
io.write_error(target_station,
"Error in input file (Buddy, {}) - {}".format(
variable, buddy_id),
error=str(e))
continue
# match the timestamps of target_station and copy over
match = np.in1d(target_station.times, buddy.times)
match_back = np.in1d(buddy.times, target_station.times)
if True in match and True in match_back:
# skip if no overlapping times at all!
all_buddy_data[bid, match] = buddy_var.data[match_back]
if diagnostics:
print("All buddies read in")
#*************************
# find differences
differences = all_buddy_data - obs_var.data
#*************************
# find spread of differences on monthly basis (with minimum value)
spreads = np.ma.zeros(differences.shape)
for month in range(1, 13):
month_locs = np.where(target_station.months == month)
for bid, buddy in enumerate(differences):
if len(differences[bid, month_locs].compressed()
) > utils.DATA_COUNT_THRESHOLD:
this_spread = utils.spread(differences[bid, month_locs])
if this_spread < MIN_SPREAD:
spreads[bid, month_locs] = MIN_SPREAD
else:
spreads[bid, month_locs] = utils.spread(
differences[bid, month_locs])
else:
spreads[bid, month_locs] = MIN_SPREAD
spreads.mask = np.copy(differences.mask)
# store which entries may be sufficient to flag
dubious = np.ma.zeros(differences.shape)
dubious.mask = np.copy(differences.mask)
#*************************
# adjust for storms
if variable in ["sea_level_pressure", "station_level_pressure"]:
distant, = np.where(initial_neighbours[:, 1].astype(int) > 100)
if len(distant) > 0:
# find positive and negative differences across neighbours
positive = np.ma.where(
differences[distant] > spreads[distant] * SPREAD_LIMIT)
negative = np.ma.where(
differences[distant] < spreads[distant] * SPREAD_LIMIT)
# spin through each neighbour
for dn, dist_neigh in enumerate(distant):
pos, = np.where(positive[0] == dn)
neg, = np.where(negative[0] == dn)
if len(neg) > 0:
ratio = len(neg) / (len(pos) + len(neg))
if ratio > 0.667:
# majority negative, only flag the positives [definitely not storms]
dubious[dist_neigh, positive[1][pos]] = 1
else:
# all stations close by so storms shouldn't affect, include all
# note where differences exceed the spread
dubious_locs = np.ma.where(
np.ma.abs(differences) > spreads * SPREAD_LIMIT)
dubious[dubious_locs] = 1
else:
#*************************
# note where differences exceed the spread [all non pressure variables]
dubious_locs = np.ma.where(
np.ma.abs(differences) > spreads * SPREAD_LIMIT)
dubious[dubious_locs] = 1
if diagnostics:
print("cross checks complete - assessing all outcomes")
#*************************
# sum across neighbours
neighbour_count = np.ma.count(differences, axis=0)
dubious_count = np.ma.sum(dubious, axis=0)
# flag if large enough fraction (>0.66)
sufficient, = np.ma.where(dubious_count > 0.66 * neighbour_count)
flags[sufficient] = "N"
if plots:
for flag in sufficient:
plot_neighbour_flags(target_station.times, flag, obs_var,
all_buddy_data)
# append flags to object
obs_var.flags = utils.insert_flags(obs_var.flags, flags)
if diagnostics:
print("Neighbour Outlier {}".format(obs_var.name))
print(" Cumulative number of flags set: {}".format(
len(np.where(flags != "")[0])))
return # neighbour_outlier
def monthly_gap(obs_var, station, config_file, plots=False, diagnostics=False):
"""
Use distribution to identify assymetries.
:param MetVar obs_var: meteorological variable object
:param Station station: station object
:param str config_file: configuration file to store critical values
:param bool plots: turn on plots
:param bool diagnostics: turn on diagnostic output
"""
flags = np.array(["" for i in range(obs_var.data.shape[0])])
all_years = np.unique(station.years)
for month in range(1, 13):
month_averages = prepare_monthly_data(obs_var,
station,
month,
diagnostics=diagnostics)
# read in the scaling
try:
climatology = float(
utils.read_qc_config(config_file,
"MDISTRIBUTION-{}".format(obs_var.name),
"{}-clim".format(month)))
spread = float(
utils.read_qc_config(config_file,
"MDISTRIBUTION-{}".format(obs_var.name),
"{}-spread".format(month)))
except KeyError:
print("Information missing in config file")
find_monthly_scaling(obs_var,
station,
config_file,
diagnostics=diagnostics)
climatology = float(
utils.read_qc_config(config_file,
"MDISTRIBUTION-{}".format(obs_var.name),
"{}-clim".format(month)))
spread = float(
utils.read_qc_config(config_file,
"MDISTRIBUTION-{}".format(obs_var.name),
"{}-spread".format(month)))
if climatology == utils.MDI and spread == utils.MDI:
# these weren't calculable, move on
continue
standardised_months = (month_averages - climatology) / spread
bins = utils.create_bins(standardised_months, BIN_WIDTH, obs_var.name)
hist, bin_edges = np.histogram(standardised_months, bins)
# flag months with very large offsets
bad, = np.where(np.abs(standardised_months) >= LARGE_LIMIT)
# now follow flag locations back up through the process
for bad_month_id in bad:
# year ID for this set of calendar months
locs, = np.where(
np.logical_and(station.months == month,
station.years == all_years[bad_month_id]))
flags[locs] = "D"
# walk distribution from centre to find assymetry
sort_order = standardised_months.argsort()
mid_point = len(standardised_months) // 2
good = True
step = 1
bad = []
while good:
if standardised_months[sort_order][
mid_point -
step] != standardised_months[sort_order][mid_point + step]:
suspect_months = [np.abs(standardised_months[sort_order][mid_point - step]), \
np.abs(standardised_months[sort_order][mid_point + step])]
if min(suspect_months) != 0:
# not all clustered at origin
if max(suspect_months) / min(suspect_months) >= 2. and min(
suspect_months) >= 1.5:
# at least 1.5x spread from centre and difference of two in location (longer tail)
# flag everything further from this bin for that tail
if suspect_months[0] == max(suspect_months):
# LHS has issue (remember that have removed the sign)
bad = sort_order[:mid_point - (
step -
1)] # need -1 given array indexing standards
elif suspect_months[1] == max(suspect_months):
# RHS has issue
bad = sort_order[mid_point + step:]
good = False
step += 1
if (mid_point - step) < 0 or (
mid_point + step) == standardised_months.shape[0]:
# reached end
break
# now follow flag locations back up through the process
for bad_month_id in bad:
# year ID for this set of calendar months
locs, = np.where(
np.logical_and(station.months == month,
station.years == all_years[bad_month_id]))
flags[locs] = "D"
if plots:
import matplotlib.pyplot as plt
plt.step(bins[1:], hist, color='k', where="pre")
if len(bad) > 0:
bad_hist, dummy = np.histogram(standardised_months[bad], bins)
plt.step(bins[1:], bad_hist, color='r', where="pre")
plt.ylabel("Number of Months")
plt.xlabel(obs_var.name.capitalize())
plt.title("{} - month {}".format(station.id, month))
plt.show()
# append flags to object
obs_var.flags = utils.insert_flags(obs_var.flags, flags)
if diagnostics:
print("Distribution (monthly) {}".format(obs_var.name))
print(" Cumulative number of flags set: {}".format(
len(np.where(flags != "")[0])))
return # monthly_gap
def repeating_value(obs_var,
times,
config_file,
plots=False,
diagnostics=False):
"""
AKA straight string
Use config file to read threshold values. Then find strings which exceed threshold.
:param MetVar obs_var: meteorological variable object
:param array times: array of times (usually in minutes)
:param str config_file: configuration file to store critical values
:param bool plots: turn on plots
:param bool diagnostics: turn on diagnostic output
"""
# remove calm periods for wind speeds when (a) calculating thresholds and (b) identifying streaks
this_var = copy.deepcopy(obs_var)
if obs_var.name == "wind_speed":
calms, = np.ma.where(this_var.data == 0)
this_var.data[calms] = utils.MDI
this_var.data.mask[calms] = True
flags = np.array(["" for i in range(this_var.data.shape[0])])
compressed_flags = np.array(
["" for i in range(this_var.data.compressed().shape[0])])
# retrieve the threshold and store in another dictionary
threshold = {}
try:
th = utils.read_qc_config(config_file,
"STREAK-{}".format(this_var.name),
"Straight")
threshold["Straight"] = float(th)
except KeyError:
# no threshold set
print("Threshold missing in config file")
get_repeating_string_threshold(this_var,
config_file,
plots=plots,
diagnostics=diagnostics)
th = utils.read_qc_config(config_file,
"STREAK-{}".format(this_var.name),
"Straight")
threshold["Straight"] = float(th)
# only process further if there is enough data
if len(this_var.data.compressed()) > 1:
repeated_string_lengths, grouped_diffs, strings = prepare_data_repeating_string(
this_var, plots=plots, diagnostics=diagnostics)
# above threshold
bad, = np.where(repeated_string_lengths >= threshold["Straight"])
# flag identified strings
for string in bad:
start = int(np.sum(grouped_diffs[:strings[string], 1]))
end = start + int(grouped_diffs[strings[string], 1]) + 1
compressed_flags[start:end] = "K"
if plots:
plot_streak(times, this_var, start, end)
# undo compression and write into original object (the one with calm periods)
flags[this_var.data.mask == False] = compressed_flags
obs_var.flags = utils.insert_flags(obs_var.flags, flags)
if diagnostics:
print("Repeated Strings {}".format(this_var.name))
print(" Cumulative number of flags set: {}".format(
len(np.where(flags != "")[0])))
return # repeating_value
def variance_check(obs_var,
station,
config_file,
plots=False,
diagnostics=False,
winsorize=True):
"""
Use distribution to identify threshold values. Then also store in config file.
:param MetVar obs_var: meteorological variable object
:param Station station: station object
:param str config_file: configuration file to store critical values
:param bool plots: turn on plots
:param bool diagnostics: turn on diagnostic output
:param bool winsorize: apply winsorization at 5%/95%
"""
flags = np.array(["" for i in range(obs_var.data.shape[0])])
# get hourly climatology for each month
for month in range(1, 13):
month_locs, = np.where(station.months == month)
variances = prepare_data(obs_var,
station,
month,
diagnostics=diagnostics,
winsorize=winsorize)
try:
average_variance = float(
utils.read_qc_config(config_file,
"VARIANCE-{}".format(obs_var.name),
"{}-average".format(month)))
variance_spread = float(
utils.read_qc_config(config_file,
"VARIANCE-{}".format(obs_var.name),
"{}-spread".format(month)))
except KeyError:
print("Information missing in config file")
find_thresholds(obs_var,
station,
config_file,
plots=plots,
diagnostics=diagnostics)
average_variance = float(
utils.read_qc_config(config_file,
"VARIANCE-{}".format(obs_var.name),
"{}-average".format(month)))
variance_spread = float(
utils.read_qc_config(config_file,
"VARIANCE-{}".format(obs_var.name),
"{}-spread".format(month)))
if average_variance == utils.MDI and variance_spread == utils.MDI:
# couldn't be calculated, move on
continue
bad_years, = np.where(
np.abs(variances - average_variance) /
variance_spread > SPREAD_THRESHOLD)
# prepare wind and pressure data in case needed to check for storms
if obs_var.name in [
"station_level_pressure", "sea_level_pressure", "wind_speed"
]:
wind_monthly_data = station.wind_speed.data[month_locs]
if obs_var.name in [
"station_level_pressure", "sea_level_pressure"
]:
pressure_monthly_data = obs_var.data[month_locs]
else:
pressure_monthly_data = station.sea_level_pressure.data[
month_locs]
if len(pressure_monthly_data.compressed()) < utils.DATA_COUNT_THRESHOLD or \
len(wind_monthly_data.compressed()) < utils.DATA_COUNT_THRESHOLD:
# need sufficient data to work with for storm check to work, else can't tell
# move on
continue
wind_average = utils.average(wind_monthly_data)
wind_spread = utils.spread(wind_monthly_data)
pressure_average = utils.average(pressure_monthly_data)
pressure_spread = utils.spread(pressure_monthly_data)
# go through each bad year for this month
all_years = np.unique(station.years)
for year in bad_years:
# corresponding locations
ym_locs, = np.where(
np.logical_and(station.months == month,
station.years == all_years[year]))
# if pressure or wind speed, need to do some further checking before applying flags
if obs_var.name in [
"station_level_pressure", "sea_level_pressure",
"wind_speed"
]:
# pull out the data
wind_data = station.wind_speed.data[ym_locs]
if obs_var.name in [
"station_level_pressure", "sea_level_pressure"
]:
pressure_data = obs_var.data[ym_locs]
else:
pressure_data = station.sea_level_pressure.data[ym_locs]
# need sufficient data to work with for storm check to work, else can't tell
if len(pressure_data.compressed()) < utils.DATA_COUNT_THRESHOLD or \
len(wind_data.compressed()) < utils.DATA_COUNT_THRESHOLD:
# move on
continue
# find locations of high wind speeds and low pressures, cross match
high_winds, = np.ma.where(
(wind_data - wind_average) / wind_spread > STORM_THRESHOLD)
low_pressures, = np.ma.where(
(pressure_average - pressure_data) /
pressure_spread > STORM_THRESHOLD)
match = np.in1d(high_winds, low_pressures)
couldbe_storm = False
if len(match) > 0:
# this could be a storm, either at tropical station (relatively constant pressure)
# or out of season in mid-latitudes.
couldbe_storm = True
if obs_var.name in [
"station_level_pressure", "sea_level_pressure"
]:
diffs = np.ma.diff(pressure_data)
elif obs_var.name == "wind_speed":
diffs = np.ma.diff(wind_data)
# count up the largest number of sequential negative and positive differences
negs, poss = 0, 0
biggest_neg, biggest_pos = 0, 0
for diff in diffs:
if diff > 0:
if negs > biggest_neg: biggest_neg = negs
negs = 0
poss += 1
else:
if poss > biggest_pos: biggest_pos = poss
poss = 0
negs += 1
if (biggest_neg < 10) and (biggest_pos <
10) and not couldbe_storm:
# insufficient to identify as a storm (HadISD values)
# leave flags set
pass
else:
# could be a storm, so better to leave this month unflagged
# zero length array to flag
ym_locs = np.ma.array([])
# copy over the flags, if any
if len(ym_locs) != 0:
# and set the flags
flags[ym_locs] = "V"
# diagnostic plots
if plots:
import matplotlib.pyplot as plt
scaled_variances = ((variances - average_variance) /
variance_spread)
bins = utils.create_bins(scaled_variances, 0.25, obs_var.name)
hist, bin_edges = np.histogram(scaled_variances, bins)
plt.clf()
plt.step(bins[1:], hist, color='k', where="pre")
plt.yscale("log")
plt.ylabel("Number of Months")
plt.xlabel("Scaled {} Variances".format(obs_var.name.capitalize()))
plt.title("{} - month {}".format(station.id, month))
plt.ylim([0.1, max(hist) * 2])
plt.axvline(SPREAD_THRESHOLD, c="r")
plt.axvline(-SPREAD_THRESHOLD, c="r")
bad_hist, dummy = np.histogram(scaled_variances[bad_years], bins)
plt.step(bins[1:], bad_hist, color='r', where="pre")
plt.show()
# append flags to object
obs_var.flags = utils.insert_flags(obs_var.flags, flags)
if diagnostics:
print("Variance {}".format(obs_var.name))
print(" Cumulative number of flags set: {}".format(
len(np.where(flags != "")[0])))
return # variance_check
def frequent_values(obs_var,
station,
config_file,
plots=False,
diagnostics=False):
"""
Use config file to read frequent values. Check each month to see if appear.
:param MetVar obs_var: meteorological variable object
:param Station station: station object
:param str config_file: configuration file to store critical values
:param bool plots: turn on plots
:param bool diagnostics: turn on diagnostic output
"""
flags = np.array(["" for i in range(obs_var.data.shape[0])])
all_years = np.unique(station.years)
# work through each month, and then year
for month in range(1, 13):
# read in bin-width and suspect bins for this month
try:
width = float(
utils.read_qc_config(config_file,
"FREQUENT-{}".format(obs_var.name),
"width"))
suspect_bins = utils.read_qc_config(config_file,
"FREQUENT-{}".format(
obs_var.name),
"{}".format(month),
islist=True)
except KeyError:
print("Information missing in config file")
identify_values(obs_var,
station,
config_file,
plots=plots,
diagnostics=diagnostics)
width = float(
utils.read_qc_config(config_file,
"FREQUENT-{}".format(obs_var.name),
"width"))
suspect_bins = utils.read_qc_config(config_file,
"FREQUENT-{}".format(
obs_var.name),
"{}".format(month),
islist=True)
# skip on if nothing to find
if len(suspect_bins) == 0:
continue
# work through each year
for year in all_years:
locs, = np.where(
np.logical_and(station.months == month, station.years == year))
month_data = obs_var.data[locs]
# skip if no data
if np.ma.count(month_data) == 0:
continue
month_flags = np.array(["" for i in range(month_data.shape[0])])
# adjust bin widths according to reporting accuracy
resolution = utils.reporting_accuracy(month_data)
if resolution <= 0.5:
bins = utils.create_bins(month_data, 0.5, obs_var.name)
else:
bins = utils.create_bins(month_data, 1.0, obs_var.name)
hist, bin_edges = np.histogram(month_data, bins)
# Scan through the histogram
# check if a bin is the maximum of a local area ("ROLLING")
for b, bar in enumerate(hist):
if (b > ROLLING // 2) and (b <= (len(hist) - ROLLING // 2)):
target_bins = hist[b - (ROLLING // 2):b + (ROLLING // 2) +
1]
# if sufficient obs, maximum and contains > 50% of data
if bar >= utils.DATA_COUNT_THRESHOLD:
if bar == target_bins.max():
if (bar / target_bins.sum()) > RATIO:
# this bin meets all the criteria
if bins[b] in suspect_bins:
# find observations (month & year) to flag!
flag_locs = np.where(
np.logical_and(
month_data >= bins[b],
month_data < bins[b + 1]))
month_flags[flag_locs] = "F"
# copy flags for all years into main array
flags[locs] = month_flags
# diagnostic plots
if plots:
import matplotlib.pyplot as plt
plt.step(bins[1:], hist, color='k', where="pre")
plt.yscale("log")
plt.ylabel("Number of Observations")
plt.xlabel(obs_var.name.capitalize())
plt.title("{} - month {}".format(station.id, month))
bad_hist = np.copy(hist)
for b, bar in enumerate(bad_hist):
if bins[b] not in suspect_bins:
bad_hist[b] = 0
plt.step(bins[1:], bad_hist, color='r', where="pre")
plt.show()
# append flags to object
obs_var.flags = utils.insert_flags(obs_var.flags, flags)
if diagnostics:
print("Frequent Values {}".format(obs_var.name))
print(" Cumulative number of flags set: {}".format(
len(np.where(flags != "")[0])))
return # frequent_values
def pressure_theory(sealp,
stnlp,
temperature,
times,
elevation,
plots=False,
diagnostics=False):
"""
Flag locations where difference between recorded and calculated sea-level pressure
falls outside of bounds
:param MetVar sealp: sea level pressure object
:param MetVar stnlp: station level pressure object
:param MetVar temperature: temperature object
:param array times: datetime array
:param float elevation: station elevation (m)
:param bool plots: turn on plots
:param bool diagnostics: turn on diagnostic output
"""
flags = np.array(["" for i in range(sealp.data.shape[0])])
theoretical_value = calc_slp(stnlp.data, elevation, temperature.data)
difference = sealp.data - theoretical_value
bad_locs, = np.ma.where(np.ma.abs(difference) > THEORY_THRESHOLD)
# diagnostic plots
if plots:
bins = np.arange(
np.round(np.ma.min(difference)) - 1,
np.round(np.ma.max(difference)) + 1, 0.1)
import matplotlib.pyplot as plt
plt.clf()
plt.hist(difference.compressed(), bins=bins)
plt.axvline(x=THEORY_THRESHOLD, ls="--", c="r")
plt.axvline(x=-THEORY_THRESHOLD, ls="--", c="r")
plt.xlim([bins[0] - 1, bins[-1] + 1])
plt.ylabel("Observations")
plt.xlabel("Difference (hPa)")
plt.show()
if len(bad_locs) != 0:
flags[bad_locs] = "p"
if diagnostics:
print("Pressure".format(stnlp.name))
print(
" Number of mismatches between recorded and theoretical SLPs {}"
.format(len(bad_locs)))
if plots:
for bad in bad_locs:
plot_pressure(sealp, stnlp, times, bad)
def adjust_preexisting_locs(var, flags):
# may have flags already set by previous part of test
# find these locations, and adjust new flags to these aren't added again
pre_exist = [i for i, item in enumerate(var.flags) if "p" in item]
new_flags = flags[:]
new_flags[pre_exist] = ""
return new_flags
# flag both as not sure immediately where the issue lies
stnlp.flags = utils.insert_flags(stnlp.flags,
adjust_preexisting_locs(stnlp, flags))
sealp.flags = utils.insert_flags(sealp.flags,
adjust_preexisting_locs(sealp, flags))
if diagnostics:
print("Pressure {}".format(stnlp.name))
print(" Cumulative number of flags set: {}".format(
len(np.where(flags != "")[0])))
return # pressure_theory
def pressure_offset(sealp,
stnlp,
times,
config_file,
plots=False,
diagnostics=False):
"""
Flag locations where difference between station and sea-level pressure
falls outside of bounds
:param MetVar sealp: sea level pressure object
:param MetVar stnlp: station level pressure object
:param array times: datetime array
:param str configfile: string for configuration file
:param bool plots: turn on plots
:param bool diagnostics: turn on diagnostic output
"""
flags = np.array(["" for i in range(sealp.data.shape[0])])
difference = sealp.data - stnlp.data
if len(difference.compressed()) >= utils.DATA_COUNT_THRESHOLD:
try:
average = float(
utils.read_qc_config(config_file, "PRESSURE", "average"))
spread = float(
utils.read_qc_config(config_file, "PRESSURE", "spread"))
except KeyError:
print("Information missing in config file")
average = utils.average(difference)
spread = utils.spread(difference)
if spread < MIN_SPREAD: # less than XhPa
spread = MIN_SPREAD
elif spread > MAX_SPREAD: # more than XhPa
spread = MAX_SPREAD
utils.write_qc_config(config_file,
"PRESSURE",
"average",
"{}".format(average),
diagnostics=diagnostics)
utils.write_qc_config(config_file,
"PRESSURE",
"spread",
"{}".format(spread),
diagnostics=diagnostics)
if np.abs(np.ma.mean(difference) -
np.ma.median(difference)) > THRESHOLD * spread:
if diagnostics:
print("Large difference between mean and median")
print("Likely to have two populations of roughly equal size")
print("Test won't work")
pass
else:
high, = np.ma.where(difference > (average + (THRESHOLD * spread)))
low, = np.ma.where(difference < (average - (THRESHOLD * spread)))
# diagnostic plots
if plots:
bins = np.arange(
np.round(difference.min()) - 1,
np.round(difference.max()) + 1, 0.1)
import matplotlib.pyplot as plt
plt.clf()
plt.hist(difference.compressed(), bins=bins)
plt.axvline(x=(average + (THRESHOLD * spread)), ls="--", c="r")
plt.axvline(x=(average - (THRESHOLD * spread)), ls="--", c="r")
plt.xlim([bins[0] - 1, bins[-1] + 1])
plt.ylabel("Observations")
plt.xlabel("Difference (hPa)")
plt.show()
if len(high) != 0:
flags[high] = "p"
if diagnostics:
print("Pressure".format(stnlp.name))
print(" Number of high differences {}".format(len(high)))
if plots:
for bad in high:
plot_pressure(sealp, stnlp, times, bad)
if len(low) != 0:
flags[low] = "p"
if diagnostics:
print(" Number of low differences {}".format(len(low)))
if plots:
for bad in low:
plot_pressure(sealp, stnlp, times, bad)
# only flag the station level pressure
stnlp.flags = utils.insert_flags(stnlp.flags, flags)
if diagnostics:
print("Pressure {}".format(stnlp.name))
print(" Cumulative number of flags set: {}".format(
len(np.where(flags != "")[0])))
return # pressure_offset
def monthly_clim(obs_var,
station,
config_file,
logfile="",
plots=False,
diagnostics=False,
winsorize=True):
"""
Run through the variables and pass to the Distributional Gap Checks
:param MetVar obs_var: meteorological variable object
:param Station station: station object
:param str configfile: string for configuration file
:param str logfile: string for log file
:param bool plots: turn on plots
:param bool diagnostics: turn on diagnostic output
"""
flags = np.array(["" for i in range(obs_var.data.shape[0])])
for month in range(1, 13):
month_locs, = np.where(station.months == month)
# note these are for the whole record, just this month is unmasked
normalised_anomalies = prepare_data(obs_var,
station,
month,
diagnostics=diagnostics,
winsorize=winsorize)
if len(normalised_anomalies.compressed()
) >= utils.DATA_COUNT_THRESHOLD:
bins = utils.create_bins(normalised_anomalies, BIN_WIDTH,
obs_var.name)
hist, bin_edges = np.histogram(normalised_anomalies.compressed(),
bins)
try:
upper_threshold = float(
utils.read_qc_config(
config_file, "CLIMATOLOGICAL-{}".format(obs_var.name),
"{}-uthresh".format(month)))
lower_threshold = float(
utils.read_qc_config(
config_file, "CLIMATOLOGICAL-{}".format(obs_var.name),
"{}-lthresh".format(month)))
except KeyError:
print("Information missing in config file")
find_month_thresholds(obs_var,
station,
config_file,
plots=plots,
diagnostics=diagnostics)
upper_threshold = float(
utils.read_qc_config(
config_file, "CLIMATOLOGICAL-{}".format(obs_var.name),
"{}-uthresh".format(month)))
lower_threshold = float(
utils.read_qc_config(
config_file, "CLIMATOLOGICAL-{}".format(obs_var.name),
"{}-lthresh".format(month)))
# now to find the gaps
uppercount = len(
np.where(normalised_anomalies > upper_threshold)[0])
lowercount = len(
np.where(normalised_anomalies < lower_threshold)[0])
if uppercount > 0:
gap_start = utils.find_gap(hist, bins, upper_threshold,
GAP_SIZE)
if gap_start != 0:
bad_locs, = np.ma.where(
normalised_anomalies >
gap_start) # all years for one month
# normalised_anomalies are for the whole record, just this month is unmasked
flags[bad_locs] = "C"
if lowercount > 0:
gap_start = utils.find_gap(hist,
bins,
lower_threshold,
GAP_SIZE,
upwards=False)
if gap_start != 0:
bad_locs, = np.ma.where(
normalised_anomalies <
gap_start) # all years for one month
flags[bad_locs] = "C"
# diagnostic plots
if plots:
import matplotlib.pyplot as plt
plt.clf()
plt.step(bins[1:], hist, color='k', where="pre")
plt.yscale("log")
plt.ylabel("Number of Observations")
plt.xlabel("Scaled {}".format(obs_var.name.capitalize()))
plt.title("{} - month {}".format(station.id, month))
plt.ylim([0.1, max(hist) * 2])
plt.axvline(upper_threshold, c="r")
plt.axvline(lower_threshold, c="r")
bad_locs, = np.where(flags[month_locs] == "C")
bad_hist, dummy = np.histogram(
normalised_anomalies[month_locs][bad_locs], bins)
plt.step(bins[1:], bad_hist, color='r', where="pre")
plt.show()
# append flags to object
obs_var.flags = utils.insert_flags(obs_var.flags, flags)
if diagnostics:
print("Climatological {}".format(obs_var.name))
print(" Cumulative number of flags set: {}".format(
len(np.where(flags != "")[0])))
return # monthly_clim
def all_obs_gap(obs_var, station, config_file, plots=False, diagnostics=False):
"""
Extract data for month and find secondary populations in distribution.
:param MetVar obs_var: meteorological variable object
:param Station station: station object
:param str config_file: configuration file to store critical values
:param bool plots: turn on plots
:param bool diagnostics: turn on diagnostic output
"""
flags = np.array(["" for i in range(obs_var.data.shape[0])])
for month in range(1, 13):
normalised_anomalies = prepare_all_data(obs_var,
station,
month,
config_file,
full=False,
diagnostics=diagnostics)
if (len(normalised_anomalies.compressed()) == 1
and normalised_anomalies[0] == utils.MDI):
# no data to work with for this month, move on.
continue
bins = utils.create_bins(normalised_anomalies, BIN_WIDTH, obs_var.name)
hist, bin_edges = np.histogram(normalised_anomalies, bins)
try:
upper_threshold = float(
utils.read_qc_config(config_file,
"ADISTRIBUTION-{}".format(obs_var.name),
"{}-uthresh".format(month)))
lower_threshold = float(
utils.read_qc_config(config_file,
"ADISTRIBUTION-{}".format(obs_var.name),
"{}-lthresh".format(month)))
except KeyError:
print("Information missing in config file")
find_thresholds(obs_var,
station,
config_file,
plots=plots,
diagnostics=diagnostics)
upper_threshold = float(
utils.read_qc_config(config_file,
"ADISTRIBUTION-{}".format(obs_var.name),
"{}-uthresh".format(month)))
lower_threshold = float(
utils.read_qc_config(config_file,
"ADISTRIBUTION-{}".format(obs_var.name),
"{}-lthresh".format(month)))
if upper_threshold == utils.MDI and lower_threshold == utils.MDI:
# these weren't able to be calculated, move on
continue
elif len(np.unique(normalised_anomalies)) == 1:
# all the same value, so won't be able to fit a histogram
continue
# now to find the gaps
uppercount = len(np.where(normalised_anomalies > upper_threshold)[0])
lowercount = len(np.where(normalised_anomalies < lower_threshold)[0])
month_locs, = np.where(
station.months == month) # append should keep year order
if uppercount > 0:
gap_start = utils.find_gap(hist, bins, upper_threshold, GAP_SIZE)
if gap_start != 0:
bad_locs, = np.ma.where(normalised_anomalies >
gap_start) # all years for one month
month_flags = flags[month_locs]
month_flags[bad_locs] = "d"
flags[month_locs] = month_flags
if lowercount > 0:
gap_start = utils.find_gap(hist,
bins,
lower_threshold,
GAP_SIZE,
upwards=False)
if gap_start != 0:
bad_locs, = np.ma.where(normalised_anomalies <
gap_start) # all years for one month
month_flags = flags[month_locs]
month_flags[bad_locs] = "d"
# TODO - can this bit be refactored?
# for pressure data, see if the flagged obs correspond with high winds
# could be a storm signal
if obs_var.name in [
"station_level_pressure", "sea_level_pressure"
]:
wind_monthly_data = prepare_monthly_data(
station.wind_speed, station, month)
pressure_monthly_data = prepare_monthly_data(
obs_var, station, month)
if len(pressure_monthly_data.compressed()) < utils.DATA_COUNT_THRESHOLD or \
len(wind_monthly_data.compressed()) < utils.DATA_COUNT_THRESHOLD:
# need sufficient data to work with for storm check to work, else can't tell
pass
else:
wind_monthly_average = utils.average(wind_monthly_data)
wind_monthly_spread = utils.spread(wind_monthly_data)
pressure_monthly_average = utils.average(
pressure_monthly_data)
pressure_monthly_spread = utils.spread(
pressure_monthly_data)
# already a single calendar month, so go through each year
all_years = np.unique(station.years)
for year in all_years:
# what's best - extract only when necessary but repeatedly if so, or always, but once
this_year_locs = np.where(
station.years[month_locs] == year)
if "d" not in month_flags[this_year_locs]:
# skip if you get the chance
continue
wind_data = station.wind_speed.data[month_locs][
this_year_locs]
pressure_data = obs_var.data[month_locs][
this_year_locs]
storms, = np.ma.where(
np.logical_and(
(((wind_data - wind_monthly_average) /
wind_monthly_spread) > STORM_THRESHOLD),
(((pressure_monthly_average - pressure_data
) / pressure_monthly_spread) >
STORM_THRESHOLD)))
# more than one entry - check if separate events
if len(storms) >= 2:
# find where separation more than the usual obs separation
storm_1diffs = np.ma.diff(storms)
separations, = np.where(
storm_1diffs > np.ma.median(
np.ma.diff(wind_data)))
if len(separations) != 0:
# multiple storm signals
storm_start = 0
storm_finish = separations[0] + 1
first_storm = expand_around_storms(
storms[storm_start:storm_finish],
len(wind_data))
final_storm_locs = copy.deepcopy(
first_storm)
for j in range(len(separations)):
# then do the rest in a loop
if j + 1 == len(separations):
# final one
this_storm = expand_around_storms(
storms[separations[j] + 1:],
len(wind_data))
else:
this_storm = expand_around_storms(
storms[separations[j] +
1:separations[j + 1] +
1], len(wind_data))
final_storm_locs = np.append(
final_storm_locs, this_storm)
else:
# locations separated at same interval as data
final_storm_locs = expand_around_storms(
storms, len(wind_data))
# single entry
elif len(storms) != 0:
# expand around the storm signal (rather than
# just unflagging what could be the peak and
# leaving the entry/exit flagged)
final_storm_locs = expand_around_storms(
storms, len(wind_data))
# unset the flags
if len(storms) > 0:
month_flags[this_year_locs][
final_storm_locs] = ""
# having checked for storms now store final flags
flags[month_locs] = month_flags
# diagnostic plots
if plots:
import matplotlib.pyplot as plt
plt.step(bins[1:], hist, color='k', where="pre")
plt.yscale("log")
plt.ylabel("Number of Observations")
plt.xlabel(obs_var.name.capitalize())
plt.title("{} - month {}".format(station.id, month))
plt.ylim([0.1, max(hist) * 2])
plt.axvline(upper_threshold, c="r")
plt.axvline(lower_threshold, c="r")
bad_locs, = np.where(flags[month_locs] == "d")
bad_hist, dummy = np.histogram(normalised_anomalies[bad_locs],
bins)
plt.step(bins[1:], bad_hist, color='r', where="pre")
plt.show()
# append flags to object
obs_var.flags = utils.insert_flags(obs_var.flags, flags)
if diagnostics:
print("Distribution (all) {}".format(obs_var.name))
print(" Cumulative number of flags set: {}".format(
len(np.where(flags != "")[0])))
return # all_obs_gap
def diurnal_cycle_check(obs_var, station, config_file, plots=False, diagnostics=False, best_fit_diurnal=None, best_fit_uncertainty=None):
"""
Use offset to find days where cycle doesn't match
:param MetVar obs_var: Meteorological Variable object
:param Station station: Station Object for the station
:param str configfile: string for configuration file
:param bool plots: turn on plots
:param bool diagnostics: turn on diagnostic output
"""
flags = np.array(["" for i in range(obs_var.data.shape[0])])
diurnal_offset = int(utils.read_qc_config(config_file, "DIURNAL-{}".format(obs_var.name), "peak"))
hours = np.arange(24)
hours = np.roll(hours, 11-int(diurnal_offset))
if diurnal_offset != MISSING:
if (best_fit_diurnal is None) and (best_fit_uncertainty is None):
best_fit_diurnal, best_fit_uncertainty = prepare_data(station, obs_var)
# find locations where the overall best fit does not match the daily fit
potentially_spurious = np.ones(best_fit_diurnal.shape[0])*MISSING
for d, (fit, uncertainty) in enumerate(zip(best_fit_diurnal, best_fit_uncertainty)):
if fit != MISSING:
min_range = 11 - uncertainty
max_range = 11 + uncertainty
offset_loc, = np.where(hours == fit)
# find where the best fit falls outside the range for this particular day
if offset_loc < min_range or offset_loc > max_range:
potentially_spurious[d] = 1
else:
potentially_spurious[d] = 0
# now check there are sufficient issues in running 30 day periods
"""Any periods>30 days where the diurnal cycle deviates from the expected
phase by more than this uncertainty, without three consecutive good or missing days
or six consecutive days consisting of a mix of only good or missing values, a
re deemed dubious and the entire period of data (including all non-temperature elements) is flagged"""
n_good = 0
n_miss = 0
n_not_bad = 0
total_points = 0
total_not_miss = 0
bad_locs = np.zeros(best_fit_diurnal.shape[0])
for d in range(best_fit_diurnal.shape[0]):
if potentially_spurious[d] == 1:
# if bad, just add one
n_good = 0
n_miss = 0
n_not_bad = 0
total_points += 1
total_not_miss += 1
else:
# find a non-bad value - so check previous run
# if have reached limits on good/missing
if (n_good == 3) or (n_miss == 3) or (n_not_bad >= 6):
# sufficient good missing or not bad data
if total_points >= 30:
# if have collected enough others, then set flag
if float(total_not_miss)/total_points >= 0.5:
bad_locs[d - total_points : d] = 1
# reset counters
n_good = 0
n_miss = 0
n_not_bad = 0
total_points = 0
total_not_miss = 0
# and deal with this point
total_points += 1
if potentially_spurious[d] == 0:
# if good
n_good += 1
n_not_bad += 1
if n_miss != 0:
n_miss = 0
total_not_miss += 1
elif potentially_spurious[d] == -999:
# if missing data
n_miss += 1
n_not_bad += 1
if n_good != 0:
n_good = 0
# run through all days
# find zero point of day counter in data preparation part
day_counter_start = dt.datetime(np.unique(station.years)[0], np.unique(station.months)[0], np.unique(station.days)[0])
# find the bad days in the times array
for day in bad_locs:
this_day = day_counter_start + dt.timedelta(days=int(day))
locs, = np.where(np.logical_and.reduce((station.years == this_day.year, station.months == this_day.month, station.days == this_day.day)))
flags[locs] = "U"
# append flags to object
obs_var.flags = utils.insert_flags(obs_var.flags, flags)
if diagnostics:
print("Diurnal Check {}".format(obs_var.name))
print(" Cumulative number of flags set: {}".format(len(np.where(flags != "")[0])))
else:
if diagnostics:
print("Diurnal fit not found")
return # diurnal_cycle_check
def flag_clusters(obs_var, station, plots=False, diagnostics=False):
"""
Go through the clusters of data and flag if meet requirements
:param MetVar obs_var: meteorological variable object
:param Station station: Station Object for the station
:param bool plots: turn on plots
:param bool diagnostics: turn on diagnostic output
"""
flags = np.array(["" for i in range(obs_var.data.shape[0])])
time_differences = np.diff(station.times) / np.timedelta64(1, "m")
potential_cluster_ends, = np.where(time_differences >= MIN_SEPARATION * 60)
# TODO - need explicit checks for start and end of timeseries
for ce, cluster_end in enumerate(potential_cluster_ends):
if ce == 0:
# check if cluster at start of series (long gap after a first few points)
cluster_length = station.times.iloc[
cluster_end] - station.times.iloc[0]
if cluster_length.asm8 / np.timedelta64(1, "h") < MAX_LENGTH_TIME:
# could be a cluster
if len(flags[:cluster_end + 1]) < MAX_LENGTH_OBS:
flags[:cluster_end + 1] = "o"
if plots:
plot_cluster(station, obs_var, 0, cluster_end + 1)
elif ce == len(potential_cluster_ends) - 1:
# check if cluster at end of series (long gap before last few points)
cluster_length = station.times.iloc[-1] - station.times.iloc[
cluster_end + 1] # add one to find cluster start!
if cluster_length.asm8 / np.timedelta64(1, "h") < MAX_LENGTH_TIME:
# could be a cluster
if len(flags[cluster_end + 1:]) < MAX_LENGTH_OBS:
flags[cluster_end + 1:] = "o"
if plots:
plot_cluster(station, obs_var, cluster_end + 1, -1)
if ce > 0:
# check for cluster in series.
# use previous gap > MIN_SEPARATION to define cluster and check length
cluster_length = station.times.iloc[
cluster_end] - station.times.iloc[potential_cluster_ends[
ce - 1] + 1] # add one to find cluster start!
if cluster_length.asm8 / np.timedelta64(1, "h") < MAX_LENGTH_TIME:
# could be a cluster
if len(flags[potential_cluster_ends[ce - 1] + 1:cluster_end +
1]) < MAX_LENGTH_OBS:
flags[potential_cluster_ends[ce - 1] + 1:cluster_end +
1] = "o"
if plots:
plot_cluster(station.times, obs_var,
potential_cluster_ends[ce - 1] + 1,
cluster_end + 1)
# append flags to object
obs_var.flags = utils.insert_flags(obs_var.flags, flags)
if diagnostics:
print("Odd Cluster {}".format(obs_var.name))
print(" Cumulative number of flags set: {}".format(
len(np.where(flags != "")[0])))
return # flag_clusters
